Papers by Xiaoman Lu
ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System (N18-5)
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| Challenge: | ELISA-EDL is a cross-lingual entity extraction, linking and localization system for Wikipedia languages. |
| Approach: | They propose a cross-lingual entity extraction, linking and localization system for English speakers . it extracts entities from unstructured text in any of 282 Wikipedia languages and links them to English knowledge bases . |
| Outcome: | The proposed system extracts entity mentions from Wikipedia and links them to English knowledge bases and visualizes locations related to disaster topics on a world heatmap. |
Error Analysis of Uyghur Name Tagging: Language-specific Techniques and Remaining Challenges (L18-1)
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| Challenge: | despite efforts at name tagging, there is limited understanding on the performance ceiling . despite the high-resource language, there are very few natural language processing tools available . |
| Approach: | They propose to use a machine learning model to identify Uyghur name tagger errors . they conclude that such a model is unlikely to be effective for Uygur, or low-resource languages . |
| Outcome: | The proposed model is unlikely to be effective for Uyghur, or low-resource languages in general, the authors argue . they show that the proposed model can be used for high-res languages with superficial features . |
Towards Mitigating Modality Bias in Vision-Language Models for Temporal Action Localization (2026.acl-long)
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| Challenge: | Existing vision-language models overemphasize linguistic priors, leading to modality bias. |
| Approach: | They propose a vision-language aggregation framework that mitigates modality bias in TAL by preserving vision as the dominant signal while adaptively exploiting language only when beneficial. |
| Outcome: | Experiments on THUMOS14 show that the proposed model outperforms state-of-the-art models by up to 3.2% mAP. |
Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation (2025.findings-acl)
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| Challenge: | Existing methods for enhancing response accuracy and accuracy struggle with open-domain QA tasks because they perform independent retrieval operations without maintaining a summarizing memory or using adaptive retrieval strategies. |
| Approach: | They propose a method that integrates non-parametric knowledge from external knowledge bases into models to enhance response accuracy while mitigating factual errors and hallucinations. |
| Outcome: | The proposed method improves on open-domain QA datasets and reduces noise and hallucinations due to redundant information and insufficient information integration. |
PIVOINE: Instruction Tuning for Open-world Entity Profiling (2023.findings-emnlp)
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| Challenge: | Existing methods for information extraction focus on a closed-world setting, but PIVOINE is a promising solution to tackle the open-world problem of entity profiling. |
| Approach: | They propose to develop an LLM that performs Open-world Entity Profiling with instruction tuning to extract desirable entity profiles . they construct INSTRUCTOPENWIKI, a substantial instruction-tuning dataset for Open-World Entity Profiles . |
| Outcome: | The proposed model outperforms existing methods and ChatGPT-based baselines on unseen and out-of-ontology cases. |